Automotive vehicles employ seating systems that accommodate the passengers of the vehicle. The seating systems include restraint systems that are calculated to restrain and protect the occupants in the event of a collision. The primary restraint system commonly employed in most vehicles today is the seatbelt. Seatbelts usually include a lap belt and a shoulder belt that extends diagonally across the occupant's torso from one end of the lap belt to a mounting structure located proximate to the occupant's opposite shoulder.
In addition, automotive vehicles may include supplemental restraint systems. The most common supplemental restraint system employed in automotive vehicles today is the inflatable airbag. In the event of a collision, the airbags are deployed as an additional means of restraining and protecting the occupants of the vehicle. Originally, the supplemental inflatable restraints (airbags) were deployed in the event of a collision whether or not any given seat was occupied. These supplemental inflatable restraints and their associated deployment systems are expensive and over time this deployment strategy was deemed not to be cost effective. Thus, there became a recognized need in the art for a means to control selectively the deployment of the airbags such that deployment occurs only when the seat is occupied.
Partially in response to this need, vehicle safety systems have been proposed that include vehicle occupant sensing systems capable of detecting whether or not a given seat is occupied. The systems act as a switch in controlling the deployment of a corresponding air bag. As such, if the occupant sensing device detects that a seat is unoccupied during a collision, it can prevent the corresponding air bag from deploying, thereby saving the vehicle owner the unnecessary cost of replacing the expended air bag.
Furthermore, many airbag deployment forces and speeds have generally been optimized to restrain one hundred eighty pound males because the one hundred eighty pound male represents the mean average for all types of vehicle occupants. However, the airbag deployment force and speed required to restrain a one hundred eighty pound male exceeds those required to restrain smaller occupants, such as some females and small children. Thus, there existed a recognized need in the art for occupant sensing systems that could be used to control selectively the deployment of the airbags when a person below a predetermined weight occupied the seat.
Accordingly, other vehicle safety systems have been proposed that are capable of detecting the weight of an occupant. In one such air bag system, if the occupant's weight falls below a predetermined level, then the system can suppress the inflation of the air bag or prevent the air bag from deploying. This reduces the risk of injury that the inflating air bag could otherwise cause to a smaller-sized occupant.
Also, many airbag deployment forces and speeds have generally been optimized to restrain a person sitting generally upright and towards the back of the seat. However, airbag deployment force and speed may inappropriately restrain a person sitting otherwise. Thus, there became a recognized need in the art for a way to control selectively the deployment of an airbag depending on the occupants sitting position.
Partially in response to this need, other vehicle safety systems have been proposed that are capable of detecting the position of an occupant within a seat. For example, if the system detects that the occupant is positioned toward the front of the seat, the system will suppress the inflation of the air bag or will prevent the air bag from deploying at all. This reduces the risk of injury that the inflating air bag could otherwise cause to the occupant. It can be appreciated that these occupant sensing systems provide valuable data, allowing the vehicle safety systems to function more effectively for reducing injuries to vehicle occupants.
One necessary component of each of the known systems discussed above includes some means for sensing the presence of the vehicle occupant in the seat. One such means may include a sensor device supported within the lower seat cushion of the vehicle seat, generating an output indicating the presence of an occupant in the seat.
Additionally, a number of occupancy sensing systems known in the related art teach the use of sensing processes that employ artificial neural networks (ANN). ANNs are more commonly referred to as neural networks, or simply, neural nets. The term neural net (NN) is in fact a broad term that includes many diverse models and approaches. However, the basic structure of all NNs draws a loose analogy to the parallel interconnectivity of the neurons of the human brain. In general terms, a NN is essentially an interconnected assembly of simple processing element units, or nodes. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to or learning from, a set of training patterns. The NN may simply have an input and an output layer of units or may have an additional “hidden” layer or layers of units that internally direct the interconnection processes. The benefit of employing a NN approach is that, if properly trained, the NN will be able to generalize and infer the correct output responses from limited input data. This not only allows the NN based occupancy sensing systems to comply with the current federal standards, but it may also allow these systems to be refined for extending their capabilities of distinguishing between a wide variety of occupants seated in a variety of positions.
Generally speaking, if it is desired to use a NN to produce particular results from a variety of possible inputs, the NN must first be trained to understand the data it will receive. In this case, the NN is provided an input and the desired result. This training process is known as “supervised learning.” Supervised learning may be applied to a number of different known types of NNs, but when used for pattern recognition, as in determining the occupancy of a vehicle seat, a “clustering” type of NN is more accurate and efficient. Clustering NNs develop a set of “codebook vectors” that define a set of output clusters or classes. During the training of a clustering NN, the NN defines and “learns” the boundaries between its established clusters. The NN employs a “learning” rule whereby the weights of the unit connections are adjusted on the basis of the training data. The learning rule is essentially the algorithm used in the NN that will be the basis for deciding how to classifying the actual incoming data once the training is completed and the NN is put into use.
For a clustering NN to perform pattern recognition and classification of a physical presence that occupies a vehicle seat, a group of sensors arranged in an array are used to collect the raw input data. Since NNs operate digitally and the data derived from the sensor array is analog, the data must be converted to a representative digital signal for input to the NN. Typically, extensive filtering is required to compensate for shortcomings in the array or to prepare the data to work with the particular type of NN employed.
However, with NNs, overlapping of sensor data muddles the distinction of one pattern from another in certain weight ranges. This may result in inaccurate interpretation of the sensor data and possible mis-classification of the occupant.
Accordingly, there remains a need in the art for a method of discriminating occupant characteristics without adding a significant number of sensors to the system. There further remains in the art a need to override a NN in the case where the NN incorrectly classifies a seat occupant. The present invention is directed to these ends.